An overview of Chinese fine-grained sentiment analysis methods based on multi-kernel plant intelligence model

Lizhao Liu; Chenlan Yang

doi:10.1117/12.2684314

1 August 2023 An overview of Chinese fine-grained sentiment analysis methods based on multi-kernel plant intelligence model

Lizhao Liu, Chenlan Yang

Author Affiliations +

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127543W (2023) https://doi.org/10.1117/12.2684314
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

This paper proposes a new model, PGB_GPT, for Chinese fine-grained sentiment analysis, which combines Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Neural Network (GCN), and Generative Pre-Training Model (GPT). Additionally, a multi-core plant intelligent model is introduced to extract comprehensive symbolic meaning and improve the precision and accuracy of sentiment analysis. PGB_GPT outperforms other combination models and the possibility of merging a multi-core plant intelligence model with BiLSTM, GCN, and GPT for more extensive and accurate emotion analysis is highlighted. For Chinese fine-grained sentiment analysis, the PGB_GPT model combines BiLSTM, GCN, and GPT, with "P" representing "Plant Intelligence," "G" representing "Graph Convolutional Neural Network," "B" representing "Bidirectional Long Short-Term Memory," and "GPT" representing "Generative Pre-Training Model." As evidenced by the sentiment analysis dataset evaluation, each component greatly contributes to the model's enhanced performance.

Citation Download Citation

Lizhao Liu and Chenlan Yang "An overview of Chinese fine-grained sentiment analysis methods based on multi-kernel plant intelligence model", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127543W (1 August 2023); https://doi.org/10.1117/12.2684314

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